Complex scheduling models and analyses for property-based real-time embedded systems
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Date
2023
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Abstract
Modern multi core architectures and parallel applications
pose a significant challenge to the worst-case centric real-time system verification
and design efforts.
The involved model and parameter uncertainty contest the fidelity of formal real-time analyses,
which are mostly based on exact model assumptions.
In this dissertation, various approaches that can accept parameter and model uncertainty
are presented.
In an attempt to improve predictability in worst-case centric analyses, the exploration of timing predictable protocols
are examined for parallel task scheduling on multiprocessors and network-on-chip arbitration.
A novel scheduling algorithm, called stationary rigid gang scheduling, for gang tasks on multiprocessors is proposed.
In regard to fixed-priority wormhole-switched network-on-chips, a more restrictive family of transmission protocols called
simultaneous progression switching protocols is proposed with predictability enhancing properties.
Moreover, hierarchical scheduling for parallel DAG tasks under parameter
uncertainty is studied to achieve temporal- and spatial isolation.
Fault-tolerance as a supplementary reliability aspect of real-time systems
is examined, in spite of dynamic external causes of fault.
Using various job variants, which trade off increased execution time demand with increased error protection,
a state-based policy selection strategy is proposed, which provably assures an acceptable quality-of-service (QoS).
Lastly, the temporal misalignment of sensor data in sensor fusion applications
in cyber-physical systems is examined. A modular analysis based on minimal properties to obtain an upper-bound for the
maximal sensor data time-stamp difference is proposed.
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Keywords
Real-time systems, Embedded systems, Multiprocessor scheduling theory, Approximation algorithms, Parallel task scheduling, Fault-tolerance